Senior Product Manager, Support AI Experimentation

Remote, USAFull-TimeSenior

Salary125,000 - 254,000 USD per year

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Job Details

Experience: 5+ years
Required Skills: Artificial IntelligenceMachine LearningProduct ManagementData science

You are genuinely AI-native: you follow model releases, you experiment with new tools on your own time, you have real opinions about what's useful versus overhyped, and you get excited when someone says "have you seen what this new model can do?" — this is not optional, it is the core of the role.
5+ years of product management experience, with meaningful time spent on AI or ML-powered products in production; bonus if that includes conversational AI, support tooling, or customer-facing automation.
Demonstrated ability to run fast, scrappy experiments — you know how to get to a meaningful result without waiting for perfect infrastructure, and you know how to read the signal clearly when you get there.
Strong judgment about which ideas are worth pursuing: you can articulate why a particular AI approach is worth Toast's time, and you can say no (and explain why) just as confidently as you say yes.
Comfortable operating in ambiguity with a shifting portfolio — you don't need a stable roadmap handed to you, and you thrive when the work evolves quickly as the technology does.
Able to connect experimental work to business outcomes and communicate findings credibly to technical and non-technical stakeholders, from engineering partners to support operations leadership.
Experience working across team boundaries to hand off proven concepts — you know how to set a partner team up for success and don't need to own the build to feel ownership over the outcome.

Scout the AI landscape continuously — new models, tools, agents, retrieval approaches, voice systems — and rapidly develop sharp POVs on what Toast's support organization should be testing and why.
Design and run lightweight experiments to pressure-test AI-driven support concepts before they're ready for infrastructure investment, moving from hypothesis to results without waiting for perfect conditions.
Make go/no-go calls on exploration threads with clarity and conviction, synthesizing experimental findings into crisp recommendations that partner teams can act on.
Own and actively manage a shifting experimentation roadmap, knowing when to pursue a new thread, when to double down on early signal, and when to kill something that isn't working.
Translate findings into business-relevant outcomes — connecting experiment results to deflection rates, resolution speed, customer effort, and care team efficiency — so stakeholders understand what's at stake.
Collaborate closely with engineering, data science, support operations, and platform teams to hand off proven concepts and ensure experimentation outputs land in production.

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